DOI,IST REx ID,Research Group,Title of publication
10.1145/3774904.3792868,21916,ChLa,Fedivertex: A graph dataset based on decentralized Social Media
10.4230/LIPIcs.FORC.2026.2,22146,"ChLa,GradSch,MoHe",Learning rate scheduling with matrix factorization for private training
10.15479/AT-ISTA-21198,21198,"GradSch,ChLa",Data heterogeneity and personalization in federated learning
null,20256,"ToHe,ChLa",Predictive monitoring of black-box dynamical systems
null,20298,ChLa,Efficient estimation of a Gaussian mean with local differential privacy
10.52202/085713-1450,22825,"MaMo,GradSch,ChLa",Neural collapse is globally optimal in deep regularized ResNets and transformers
10.52202/085713-0278,22824,"GradSch,ChLa",Fast rate bounds for multi-task and meta-learning with different sample sizes
null,20296,"ChLa,ToHe",Logic gate neural networks are good for verification
null,20819,"ChLa,MoHe",Differentially private federated k-means clustering with server-side data
10.48550/ARXIV.2505.15579,21207,ChLa,Federated learning with unlabeled clients: Personalization can happen in low dimensions
10.52202/085713-1924,22828,"GradSch,ChLa",Continual release moment estimation with differential privacy
10.1007/s00521-024-10616-1,12662,ChLa,Generalization in multi-objective machine learning
10.15479/10.15479/at-ista-19759,19759,"GradSch,ChLa",Robust image classification with 1-Lipschitz networks
10.1109/CVPRW67362.2025.00071,20455,ChLa,Intriguing properties of robust classification
10.52202/085713-4798,22823,ChLa,Setting ε is not the issue in differential privacy
null,19408,ChLa,Continual learning: Applications and the road forward
10.48550/arXiv.2403.06833,19063,"GradSch,ChLa",Can LLMs separate instructions from data? And what do we even mean by that?
null,17411,ChLa,PEFLL: Personalized federated learning by learning to learn
null,18120,ChLa,Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials
null,18118,ChLa,More flexible PAC-Bayesian meta-learning by learning learning algorithms